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20212024
most citedUncertainty Quantification for Traffic Forecasting: A Unified Approach

7 citations · 25 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DB2024★ 5 cited

A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data

Hao Miao, Yan Zhao, Chenjuan Guo +5

The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e.g., traffic prediction, human mobility m…

cs.LG2022★ 1 cited

Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting

Yunyao Cheng, Chenjuan Guo, Kaixuan Chen +6

Accurate time series forecasting is crucial for optimizing resource allocation, industrial production, and urban management, particularly with the growth of cyber-physical and IoT…

cs.LG2022★ 4 cited

A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis

Yan Zhao, Liwei Deng, Xuanhao Chen +7

The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energ…

cs.LG2022★ 7 cited

Uncertainty Quantification for Traffic Forecasting: A Unified Approach

Weizhu Qian, Dalin Zhang, Yan Zhao +2

Uncertainty is an essential consideration for time series forecasting tasks. In this work, we specifically focus on quantifying the uncertainty of traffic forecasting. To achieve t…

cs.LG2022★ 3 cited

Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version

Tung Kieu, Bin Yang, Chenjuan Guo +4

Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-…

cs.LG2021★ 5 cited

Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting

Yue Cui, Jiandong Xie, Kai Zheng

Long sequence time-series forecasting (LSTF) has become increasingly popular for its wide range of applications. Though superior models have been proposed to enhance the prediction…